@inproceedings{947f71fdbe754be787ba2a3e3ed27480,
title = "On Improving Convolutional Networks Based People Detection with Fisheye Cameras",
abstract = "In this paper, we propose a new method to train convolutional neural networks for detecting people in images taken with ceiling-mounted fisheye cameras. While simply fine-tune existing detectors using annotated images lead to increased false positives due to lack of variety in the training data, we find that adding automatically computed backgrounds of the target scene in the training process yields much better detection accuracies. This allows us to build practical scene-specific human detectors.",
keywords = "Background modeling, Fisheye cameras, Human detection, Omnivision cameras, People detection, Transfer learning",
author = "Hsieh, {Yun Yi} and Chiang, {Sheng Ho} and Tsaipei Wang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021 ; Conference date: 16-11-2021 Through 19-11-2021",
year = "2021",
doi = "10.1109/ISPACS51563.2021.9651043",
language = "English",
series = "ISPACS 2021 - International Symposium on Intelligent Signal Processing and Communication Systems: 5G Dream to Reality, Proceeding",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ISPACS 2021 - International Symposium on Intelligent Signal Processing and Communication Systems",
address = "美國",
}